code: def _check_op_dense(op, numpy_inputs, **kwargs):
      tf_inputs = [tf.convert_to_tensor(x) for x in numpy_inputs]
      tf_inputs = _cast_all_to_dtype(tf_inputs, np.float32)
  
      output = op(*tf_inputs, **kwargs)
      if isinstance(output, tf.SparseTensor):
          # Sometimes ops with dense inputs return sparse tensors
          return tf.sparse.to_dense(output).numpy()
      return np.asarray(output)
  
imports: import numpy
  import scipy.sparse
  import tensorflow
  from spektral.data.utils import to_disjoint
  from spektral.layers import ops
  from spektral.utils import convolution
  from spektral.utils.sparse import sp_batch_to_sp_tensor, sp_matrix_to_sp_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _check_op_dense(op, numpy_inputs) and returns: np, tf